Cytosolic and mitochondrial NADPH fluxes are independently regulated.

Although nicotinamide adenine dinucleotide phosphate (NADPH) is produced and consumed in both the cytosol and mitochondria, the relationship between NADPH fluxes in each compartment has been difficult to assess due to technological limitations. Here we introduce an approach to resolve cytosolic and mitochondrial NADPH fluxes that relies on tracing deuterium from glucose to metabolites of proline biosynthesis localized to either the cytosol or mitochondria. We introduced NADPH challenges in either the cytosol or mitochondria of cells by using isocitrate dehydrogenase mutations, administering chemotherapeutics or with genetically encoded NADPH oxidase. We found that cytosolic challenges influenced NADPH fluxes in the cytosol but not NADPH fluxes in mitochondria, and vice versa. This work highlights the value of using proline labeling as a reporter system to study compartmentalized metabolism and reveals that NADPH homeostasis in the cytosolic and mitochondrial locations of a cell are independently regulated, with no evidence for NADPH shuttle activity.

Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify, and Validate Metabolites in Untargeted Analyses.

While the combination of liquid chromatography and tandem mass spectrometry (LC-MS/MS) is commonly used for feature annotation in untargeted omics experiments, ensuring these prioritized features originate from endogenous metabolism remains challenging. Isotopologue workflows, such as isotopic ratio outlier analysis (IROA), mass isotopomer ratio analysis of U-13C labeled extracts (MIRACLE), and credentialing incorporate isotopic labels directly into metabolic precursors, guaranteeing that all features of interest are unequivocal byproducts of cellular metabolism. Furthermore, comprehensive separation and annotation of small molecules continue to challenge the metabolomics field, particularly for isomeric systems. In this paper, we evaluate the analytical utility of incorporating ion mobility spectrometry (IMS) as an additional separation mechanism into standard LC-MS/MS isotopologue workflows. Since isotopically labeled molecules codrift in the IMS dimension with their 12C versions, LC-IMS-CID-MS provides four dimensions (LC, IMS, MS, and MS/MS) to directly investigate the metabolic activity of prioritized untargeted features. Here, we demonstrate this additional selectivity by showcasing how a preliminary data set of 30 endogeneous metabolites are putatively annotated from isotopically labeled Escherichia coli cultures when analyzed by LC-IMS-CID-MS. Metabolite annotations were based on several molecular descriptors, including accurate mass measurement, carbon number, annotated fragmentation spectra, and collision cross section (CCS), collectively illustrating the importance of incorporating IMS into isotopologue workflows. Overall, our results highlight the enhanced separation space and increased annotation confidence afforded by IMS for metabolic characterization and provide a unique perspective for future developments in isotopically labeled MS experiments.

AutoProteome Chip System for Fully Automated and Integrated Proteomics Sample Preparation and Peptide Fractionation.

With recent advances in LC-MS systems, current MS-based proteomics has an increasing need for automated, high-throughput sample preparation with neglectable sample loss. In this study, we developed a microfluidic system for fully automated proteomics sample preparation. All of the required proteomics sample preparation steps for both protein digestion and peptide fractionation are fully integrated into a disposable plastic chip device (named AutoProteome Chip). The AutoProteome Chip packed with mixed-mode ion exchange beads and C18 membrane in tandem could be fabricated with very low cost and high stability in organic reagents. Benefiting from its low backpressure, the AutoProteome Chip could be precisely driven by gas pressure, which could be easily multiplexed. As low as 2 ng of standard protein BSA could be trapped into the AutoProteome chip and processed within 2 h. Fully automated processing of 10 µg of protein extracts of HEK 293T cells achieved more than 97% of digestion efficiency with missed cleavage less than 2 and comparable performance with conventional approaches. More than 4700 proteins could be readily identified within 80 min of LC-MS analysis with good label-free quantification performance (Pearson correlation coefficient >0.99). Furthermore, deep proteome profiling by integrated high-pH RP fractionation in the same AutoProteome Chip resulted in more than 7500 proteins being identified from only 20 µg of protein extracts of HEK 293T cells and comparable reprodicibility as single-shot analysis. The AutoProteome Chip system provided a valuable prototype for developing a fully automated proteome analysis workflow and for proteomic applications with high demand for processing throughput, reproducibility, and sensitivity.

Dose-Response Metabolomics To Understand Biochemical Mechanisms and Off-Target Drug Effects with the TOXcms Software.

Small-molecule drugs and toxicants commonly interact with more than a single protein target, each of which may have unique effects on cellular phenotype. Although untargeted metabolomics is often applied to understand the mode of action of these chemicals, simple pairwise comparisons of treated and untreated samples are insufficient to resolve the effects of disrupting two or more independent protein targets. Here, we introduce a workflow for dose-response metabolomics to evaluate chemicals that potentially affect multiple proteins with different potencies. Our approach relies on treating samples with various concentrations of compound prior to analysis with mass spectrometry-based metabolomics. Data are then processed with software we developed called TOXcms, which statistically evaluates dose-response trends for each metabolomic signal according to user-defined tolerances and subsequently groups those that follow the same pattern. Although TOXcms was built upon the XCMS framework, it is compatible with any metabolomic data-processing software. Additionally, to enable correlation of dose responses beyond those that can be measured by metabolomics, TOXcms also accepts data from respirometry, cell death assays, other omic platforms, etc. In this work, we primarily focus on applying dose-response metabolomics to find off-target effects of drugs. Using metformin and etomoxir as examples, we demonstrate that each group of dose-response patterns identified by TOXcms signifies a metabolic response to a different protein target with a unique drug binding affinity. TOXcms is freely available on our laboratory website at http://pattilab.wustl.edu/software/toxcms .

Alteration of Cellular Reduction Potential Will Change 64Cu-ATSM Signal With or Without Hypoxia.

Therapies targeting reductive/oxidative (redox) metabolism hold potential in cancers resistant to chemotherapy and radiation. A redox imaging marker would help identify cancers susceptible to redox-directed therapies. Copper(II)-diacetyl-bis(4-methylthiosemicarbazonato) (Cu-ATSM) is a PET tracer developed for hypoxia imaging that could potentially be used for this purpose. We aimed to demonstrate that Cu-ATSM signal is dependent on cellular redox state, irrespective of hypoxia. Methods: We investigated the relationship between 64Cu-ATSM signal and redox state in human cervical and colon cancer cells. We altered redox state using drug strategies and single-gene mutations in isocitrate dehydrogenases (IDH1/2). Concentrations of reducing molecules were determined by spectrophotometry and liquid chromatography-mass spectrometry and compared with 64Cu-ATSM signal in vitro. Mouse models of cervical cancer were used to evaluate the relationship between 64Cu-ATSM signal and levels of reducing molecules in vivo, as well as to evaluate the change in 64Cu-ATSM signal after redox-active drug treatment. Results: A correlation exists between baseline 64Cu-ATSM signal and cellular concentration of glutathione, nicotinamide adenine dinucleotide phosphate (NADPH), and nicotinamide adenine dinucleotide (NADH). Altering NADH and NADPH metabolism using drug strategies and IDH1 mutations resulted in significant changes in 64Cu-ATSM signal under normoxic conditions. Hypoxia likewise changed 64Cu-ATSM signal, but treatment of hypoxic cells with redox-active drugs resulted in a more dramatic change than hypoxia alone. A significant difference in NADPH was seen between cervical tumor orthotopic implants in vivo, without a corresponding difference in 64Cu-ATSM signal. After treatment with ß-lapachone, there was a change in 64Cu-ATSM signal in xenograft tumors smaller than 50 mg but not in larger tumors. Conclusion:64Cu-ATSM signal reflects redox state, and altering redox state impacts 64Cu-ATSM metabolism. Our animal data suggest there are other modulating factors in vivo. These findings have implications for the use of 64Cu-ATSM as a predictive marker for redox therapies, though further in vivo work is needed.

Hepatic monoacylglycerol acyltransferase 1 is induced by prolonged food deprivation to modulate the hepatic fasting response.

During prolonged fasting, the liver plays a central role in maintaining systemic energy homeostasis by producing glucose and ketones in processes fueled by oxidation of fatty acids liberated from adipose tissue. In mice, this is accompanied by transient hepatic accumulation of glycerolipids. We found that the hepatic expression of monoacylglycerol acyltransferase 1 (Mogat1), an enzyme with monoacylglycerol acyltransferase (MGAT) activity that produces diacyl-glycerol from monoacylglycerol, was significantly increased in the liver of fasted mice compared with mice given ad libitum access to food. Basal and fasting-induced expression of Mogat1 was markedly diminished in the liver of mice lacking the transcription factor PPARα. Suppressing Mogat1 expression in liver and adipose tissue with antisense oligonucleotides (ASOs) reduced hepatic MGAT activity and triglyceride content compared with fasted controls. Surprisingly, the expression of many other PPARα target genes and PPARα activity was also decreased in mice given Mogat1 ASOs. When mice treated with control or Mogat1 ASOs were gavaged with the PPARα ligand, WY-14643, and then fasted for 18 h, WY-14643 administration reversed the effects of Mogat1 ASOs on PPARα target gene expression and liver triglyceride content. In conclusion, Mogat1 is a fasting-induced PPARα target gene that may feed forward to regulate liver PPARα activity during food deprivation.

A Protocol to Compare Methods for Untargeted Metabolomics.

There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).

Sorting cells alters their redox state and cellular metabolome.

A growing appreciation of the metabolic artifacts of cell culture has generated heightened enthusiasm for performing metabolomics on populations of cells purified from tissues and biofluids. Fluorescence activated cell sorting, or FACS, is a widely used experimental approach to purify specific cell types from complex heterogeneous samples. Here we show that FACS introduces oxidative stress and alters the metabolic state of cells. Compared to unsorted controls, astrocytes subjected to FACS prior to metabolomic analysis showed altered ratios of GSSG to GSH, NADPH to NADP+, and NAD+ to NADH. Additionally, a 50% increase in reactive oxygen species was observed in astrocytes subjected to FACS relative to unsorted controls. At a more comprehensive scale, nearly half of the metabolomic features that we profiled by liquid chromatography/mass spectrometry were changed by at least 1.5-fold in intensity due to cell sorting. Some specific metabolites identified to have significantly altered levels as a result of cell sorting included glycogen, nucleosides, amino acids, central carbon metabolites, and acylcarnitines. Although the addition of fetal bovine serum to the cell-sorting buffer decreased oxidative stress and attenuated changes in metabolite concentrations, fetal bovine serum did not preserve the metabolic state of the cells during FACS. We conclude that, irrespective of buffer components and data-normalization strategies we examined, metabolomic results from sorted cells do not accurately reflect physiological conditions prior to sorting.

Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome.

Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C8 column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C8 RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics. Graphical abstract Analysis of semipolar and nonpolar metabolites necessitates two reversed-phase chromatography (RPLC) methods, which extend metabolome coverage more than fivefold for untargeted profiling. HILIC hydrophilic interaction liquid chromatography.

Simple and Integrated Spintip-Based Technology Applied for Deep Proteome Profiling.

Great efforts have been taken for developing high-sensitive mass spectrometry (MS)-based proteomic technologies, among which sample preparation is one of the major focus. Here, a simple and integrated spintip-based proteomics technology (SISPROT) consisting of strong cation exchange beads and C18 disk in one pipet tip was developed. Both proteomics sample preparation steps, including protein preconcentration, reduction, alkylation, and digestion, and reversed phase (RP)-based desalting and high-pH RP-based peptide fractionation can be achieved in a fully integrated manner for the first time. This easy-to-use technology achieved high sensitivity with negligible sample loss. Proteomic analysis of 2000 HEK 293 cells readily identified 1270 proteins within 1.4 h of MS time, while 7826 proteins were identified when 100000 cells were processed and analyzed within only 22 h of MS time. More importantly, the SISPROT can be easily multiplexed on a standard centrifuge with good reproducibility (Pearson correlation coefficient > 0.98) for both single-shot analysis and deep proteome profiling with five-step high-pH RP fractionation. The SISPROT was exemplified by the triplicate analysis of 100000 stem cells from human exfoliated deciduous teeth (SHED). This led to the identification of 9078 proteins containing 3771 annotated membrane proteins, which was the largest proteome data set for dental stem cells reported to date. We expect that the SISPROT will be well suited for deep proteome profiling for fewer than 100000 cells and applied for translational studies where multiplexed technology with good label-free quantification precision is required.